Zero-Shot Image Classification
Transformers
PyTorch
Safetensors
vision-text-dual-encoder
image generation
visual qa
text-image embedding
image-text embedding
sartify
visual conversional ai
image semantic retrival
african raw resourced languages
Instructions to use sartifyllc/AViLaMa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sartifyllc/AViLaMa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sartifyllc/AViLaMa") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("sartifyllc/AViLaMa") model = AutoModel.from_pretrained("sartifyllc/AViLaMa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae8bdb22466e5610d9a5dad76322a5da1156040ddb942c5a5b1ba8891695f8d8
- Size of remote file:
- 3.1 GB
- SHA256:
- e194f8702917d0f348bbce0cea6fe6141d898bce10e476edd8989cbf4ebe6ef6
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